In fact, the DL-based algorithms should adapt to the environmental changes and customize the antenna extrapolation schemes according to the environmental information.
Hence, once the environment changes, say when the users are moving, the corresponding NNs should be re-trained.
Thus, how to achieve a good balance between the performance gains and the cost caused from retraining the DNN is another challenge for the antenna extrapolation.
A possible solution could be the transfer and meta learning that has the ability to adapt to the new environment quickly with a small amount of new training samples [1]}.
